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Duplicate from ellyothim/First_shot_gradio_covid_sentiment_analysis

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Co-authored-by: Othim <ellyothim@users.noreply.huggingface.co>

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  1. .gitattributes +35 -0
  2. README.md +14 -0
  3. app.py +57 -0
  4. requirements.txt +6 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: First Shot Gradio Covid Sentiment Analysis
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+ emoji: 🔥
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+ colorFrom: yellow
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+ colorTo: purple
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+ sdk: gradio
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+ sdk_version: 3.38.0
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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+ duplicated_from: ellyothim/First_shot_gradio_covid_sentiment_analysis
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import gradio as gr
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+
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+ from transformers import AutoModelForSequenceClassification
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+ from transformers import TFAutoModelForSequenceClassification
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+ from transformers import AutoTokenizer, AutoConfig
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+ import numpy as np
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+ from scipy.special import softmax
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+
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+
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+ # setting up the requiremnts
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+
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+ model_path = f"mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
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+ tokenizer = AutoTokenizer.from_pretrained('mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis')
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+ config = AutoConfig.from_pretrained(model_path)
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+ model = AutoModelForSequenceClassification.from_pretrained(model_path)
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+
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+ # Preprocess text (username and link placeholders)
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+ def preprocess(text):
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+ new_text = []
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+ for t in text.split(" "):
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+ t = '@user' if t.startswith('@') and len(t) > 1 else t
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+ t = 'http' if t.startswith('http') else t
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+ new_text.append(t)
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+ return " ".join(new_text)
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+
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+ # Defining the main function
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+ def sentiment_analysis(text):
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+ text = preprocess(text)
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+
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+ # PyTorch-based models
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+ encoded_input = tokenizer(text, return_tensors='pt')
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+ output = model(**encoded_input)
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+ scores_ = output[0][0].detach().numpy()
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+ scores_ = softmax(scores_)
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+
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+ # Format output dict of scores
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+ labels = ['Negative😢😢', 'Neutral', 'Positive😃😃']
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+ scores = {l:float(s) for (l,s) in zip(labels, scores_) }
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+
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+ return scores
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+
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+ welcome_message = "Welcome to Team Paris tweets first shot Sentimental Analysis App 😃 😃 😃 😃 "
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+ demo = gr.Interface(
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+ fn=sentiment_analysis,
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+ inputs=gr.Textbox(placeholder="Write your tweet here..."),
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+ outputs="label",
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+ interpretation="default",
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+ examples=[["This is wonderful!"]],
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+ title=welcome_message,
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+ description=("This is a sentimental analysis app built by fine tuning a model trained on financial news sentiment, we leverage what the model has learnt, /n, and fine tune it on twitter comments . The eval_loss of our model is 0.785")
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+ )
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+ demo.launch()
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+ # def greet(name):
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+ # return "Hello " + name + "!!"
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+
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+ # iface = gr.Interface(fn=greet, inputs="text", outputs="text")
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+ # iface.launch(inline = False)
requirements.txt ADDED
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+ transformers
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+ torch
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+ numpy
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+ gradio
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+ scipy>=1.6.0
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+